A sneak peak of a video being produced by Steven J. Barnes and Chandra Jade, to teach about Epigenetic mechanisms. The video includes sequences of stop motion animation of chalkboard drawings–animations were done by Steven J. Barnes, Instructor I in Vantage College and the Department of Psychology, University of British Columbia.
Physicists have developed a technique that can tell which parts of the brain rely on analog signals and which rely on digital signals.
One of the great debates in neuroscience is how neurons encode information that is sent to and from the brain. At issue is whether the information is sent in digital or analog form or indeed whether the brain can process both at the same time. That’s important because it can change the way we think about how the brain works.
But solving this question isn’t easy. The digital signals used by conventional computers are entirely different from the analog signals used in devices such as old-fashioned TVs and radios. That makes them easy to distinguish,
But the same can’t be said of neural signals, where digital and analog signals are hard to tell apart. So a useful step forward would be a way to distinguish between neural signals that are analog and those that are digital.
Today, Yasuhiro Mochizuki and Shigeru Shinomoto at Kyoto University in Japan say they’ve come up with just such a technique. And these guys have used it to distinguish between analog and digital signals in the brain for the first time.
Neuroscientists have long known that neurons carry signals in the form of electrical pulses that they call action potentials or spikes. A series of these is known as a spike train.
Exactly how information is encoded in a spike train isn’t known but researchers have discovered at least two different encoding protocols. In the 1990s, neuroscientists found that the way a muscle becomes tense is determined by the number of spikes in a given time interval, the rate which they arrive. This kind of signal is either on or off and so is clearly digital.
But other neuroscientists say that information can be encoded in another way–in the precise timings between single spikes as they arrive. This is analog encoding.
The difficulty is in telling these two apart since they both depend on the pattern of spikes that travel along a neuron. And that causes much dispute in the neuroscience community because nobody agrees on when a signal is analog or digital.
Now Mochizuki and Shinomoto have come up with a way to automatically distinguish between these types encoding. Their approach is based on the idea that some statistical models are better at representing digital codes than analog ones and vice versa.
For example, an approach known as empirical Bayes modelling is specifically designed to simulate analog signals. By contrast, hidden Markov modelling is particularly good at capturing the properties of digital codes.
Mochizuki and Shinomoto’s idea is to exploit the strengths of each method to determine whether a neuronal signal is digital or analog.
Their method is straightforward. They analyse a neuronal signal and then try to reproduce it using the empirical Bayes model and then using the hidden Markov model. They then decide whether it is digital or analog depending on the model that best simulates the characteristics of the original signal.
So if the empirical Bayes model best simulates the signal, it must be analog. And if the hidden Markov model triumphs then the signal must be digital.
These guys have tested their approach by analysing the signals produced in different parts of the brains of long-tailed macaques. And they say their approach indicates that different parts of the brain rely on different forms of encoding. “Fractions of neurons exhibiting analog and digital coding patterns differ between the three brain regions,” they say.
That’s an interesting discovery. If their method proves sound, it could finally help to settle the question of how the brain encodes information to do different tasks. And it could also help engineers build chips that can recreate these kinds of signals to make better interfaces between humans and machines and even to replace nerve function once it has been irreparably damaged.
There’s certainly more to come on this topic. But in the meantime, interesting stuff!
Ref: arxiv.org/abs/1311.4035: Analog And Digital Codes In The Brain
Read more at www.technologyreview.com
The effects of gravity are relevant when building houses or flying airplanes, but biologists have generally accepted that the average cell is too small for gravity to play a role in how it is built or behaves. A finding by Princeton University researchers now shows gravity imposes a size constraint on cells. The results provide a novel reason why most animal cells are small and of similar size.
While studying what makes large particles in a nucleus of the egg cells of the African clawed frog stay in place, Brangwynne and graduate student Marina Feric observed the particles falling to the bottom of the nuclei when a scaffolding inside the cells was disturbed.
The researchers, who published their findings in the October issue of Nature Cell Biology, concluded that when a cell reaches a certain size, it becomes subject to gravitational forces that require a scaffolding to stabilize the internal components.
“The research is really elegant and novel,” said Zemer Gitai, an associate professor ofmolecular biology at Princeton, who was not involved in the research. “Cells almost certainly evolved to be [small enough] to ignore the effects of gravity.”
The size is the limit
The typical animal cell has a diameter of about 10 microns (thousandths of millimeters). Larger cells, like the egg cells of the African clawed frog, are up to 1 millimeter in diameter, but examples of such large cells are not frequent. Scientists have attributed this size limit to the difficulty for large-volume cells to obtain nutrients, an explanation Brangwynne said is not backed by substantial evidence.
Brangwynne previously had shown that certain types of large particles within cells act like water droplets — they tend to merge upon contact. But in the nucleus, something was keeping them from fusing into one giant blob. The team first tested whether a scaffold was in place that allowed smaller particles to move through the mesh but caused larger particles to get stuck, preventing them from fusing. Feric tested this idea by injecting the frog egg nuclei with different sized Teflon-like beads and observed their movement. As predicted, small beads diffused throughout the nucleus but larger ones got stuck, providing evidence for a scaffold.
Feric next tested whether this matrix could be made up of fibers of the protein actin, which was known to form a cytoskeleton in the parts of cells outside of the nucleus but whose role in the nucleus was not clear.
The researchers rid the nuclei of the actin polymers, either by treating the nuclei with drugs against the protein, or by making the nucleus pump out the protein.
“When we did this experiment we found the large particles sunk like pebbles to the bottom of the nucleus. That was genuinely shocking,” said Brangwynne.
Feric also attached a fluorescent probe to the actin proteins to visualize the matrix. The size of the holes of the mesh network matched the size predicted by the bead experiments.
Noting that actin is both less abundant in smaller nuclei and does not appear to form a mesh that spans the whole nucleus as it does in larger cells, Feric’s experiments led the researchers to deduce that larger cells have the actin mesh to protect against gravity.
They propose that gravity becomes important at a certain particle density and a cell size of roughly 10 microns — the size limit of most animal cells. The actin in these large nuclei keeps the particles in place as a support against gravity.
Particles in a cell become proportionally larger with increasing cell size. A particle in a small cell is like a single piece of dust — it floats well, unhindered by gravity. But particles in larger cells are like many pieces of dust clustered together that have a greater mass and require support to stay buoyed.
Tim Mitchison, who studies nuclear structure at Harvard Medical School, said the study shows new evidence that actin provides a supportive matrix in very large cells, but notes that is not clear whether these nuclei are a model for smaller cells.
The research provides a new function for actin in the nucleus, said Dyche Mullins, a molecular biologist at the University of California San Francisco School of Medicine. “The results suggest a large cell becomes fragile and needs a scaffold inside to support and separate the large number of particles it contains,” he said.
Feric and Brangwynne plan to repeat the experiments in different-sized cells and explore the properties of the actin network in the nucleus to understand the limits of its strength.
The researchers said a rewarding aspect of the study was its surprising turns, which at one point led them to calculate the viscosity of the nucleus to understand the behavior of the beads they injected.
“We had absolutely no intention of trying to learn about gravity,” said Brangwynne.
“That you need to know the viscosity of the cell nucleus to figure out that gravity could be important for setting the upper limits of cell size? It’s hard to imagine how one could predict such a connection.”
In an undergraduate course Brangwynne teaches, students have previously performed calculations suggesting gravity is a negligible force on cells. Brangwynne said he will now have to change the exercise. “This is where the research ends up influencing the class work.”
The research was supported by a New Innovator Award from the National Institutes of Health and a Searle Scholar Award, both awarded to Brangwynne in recognition of outstanding work as a young scientist.
Stephen Hawking is known for his research into relativity, black holes, and quantum mechanics, as well as for the disease that has left him almost entirely paralyzed. But the theoretical cosmologist says that, were he to start from scratch, he wouldn’t focus on physics.
What would Stephen Hawking do with his life if he had to do it all over again? Hawking, as you know, was one of the world’s most illustrious physicists and science author. And now he’s out with a new book, a personal memoir entitled “My Brief History,” out this week from Bantam. He doesn’t give many interviews as his illness does not allow him to converse in real time, but he did agree to answer a few questions we put to him. And I think some of his responses may surprise you. Let me give you those right now. First, we asked him, are there any mysteries about the universe you think we may never be able to answer, questions beyond the reach of science?
STEPHEN HAWKING: I believe there are no questions that science can’t answer about a physical universe. Although we don’t yet have the full understanding of the laws of nature, I think we will eventually find a complete unified theory. Some people would claim that things like love, joy and beauty belong to a different category from science and can’t be described in scientific terms, but I think they can now be explained by the theory of evolution.
FLATOW: We asked him another question – and I think this gave us a really interesting answer. We asked him if you were to start your career over again now, starting now, what would you study and why?
HAWKING: If I were starting research now, I might study molecular biology, the science of life. Crick and Watson discovered the double helix structure of DNA and the genetic code in 1953. I did not realize its significance in 1957 when I had to choose a science to specialize in. In my school, the brightest boys did math and physics, the less bright did physics and chemistry and the least bright did biology. I wanted to do math and physics but my father made me do chemistry because he thought there would be no jobs for mathematicians.
FLATOW: And finally we asked him what scientific question outside of physics most intrigues you?
HAWKING: The biggest unsolved problem in science, outside physics, is the origin of life. Did it arrive spontaneously on Earth, and if so how, or did it come from another planet on a meteorite?
Michael J. West
In Moby Dick, Herman Melville wondered how – or what – whales see with eyes on opposite sides of their heads. “It is plain that he can never see an object which is exactly ahead… Is his brain so much more comprehensive, combining and subtle than man’s that he can at the same moment of time attentively examine two distinct prospects, one on one side of him, and the other in an exactly opposite direction?” he asked. It’s a good question. But if Melville were alive today he might have pondered something perhaps even more intriguing: Can whales see the stars?
Two years ago, a team led by Professor Travis Horton of the University of Canterbury in New Zealand published the most detailed study ever of the migration patterns of whales.
Using satellites, they tracked the movements of South Atlantic humpback whales over eight years.
To their surprise, the researchers found that the whales followed almost perfectly straight paths across thousands of miles of open sea, often deviating by less than one degree.
Ocean currents, storms, and varying seafloor depths – nothing seemed to knock the whales off course.
But how can humpbacks follow such straight trajectories with no landmarks to guide their way across the vast featureless seascape?
Scientists have known for decades that migrating animals use a variety of sensory cues to orient themselves, including our planet’s magnetic field and the sun’s position in the sky.
Yet the precision of the whales’ routes seems difficult to explain with those mechanisms alone.
Horton and his research team concluded, “It seems unlikely that individual magnetic and solar orientation cues can, in isolation, explain the extreme navigational precision achieved by humpback whales,” speculating that “alternative mechanisms of migratory orientation” might be at work.
An exciting – but still unproven – possibility is that whales use the stars to chart
their oceanic voyages….
…Read more at http://arxiv.org/ftp/arxiv/papers/1309/1309.2722.pdf